An Algorithm for Extracting Fuzzy Rules Based on RBF Neural Network

Author

Li, Wen ; Hori, Yoichi

Author_Institution

Univ. of Tokyo

Volume

53

Issue

4

fYear

2006

fDate

6/1/2006 12:00:00 AM

Firstpage

1269

Lastpage

1276

Abstract

A four-layer fuzzy-neural network structure and some algorithms for extracting fuzzy rules from numeric data by applying the functional equivalence between radial basis function (RBF) networks and a simplified class of fuzzy inference systems are proposed. The RBF neural network not only expresses the architecture of fuzzy systems clearly but also maintains the explanative characteristic of linguistic meaning. The fuzzy partition algorithm of input space, inference algorithm, and parameter tuning algorithm are also discussed. Simulation examples are given to illustrate the validity of the proposed algorithms

Keywords

fuzzy neural nets; inference mechanisms; radial basis function networks; RBF neural network; fuzzy inference systems; fuzzy neural network; fuzzy partition algorithm; fuzzy rules extraction; input space; radial basis function networks; Backpropagation; Data mining; Fuzzy neural networks; Fuzzy systems; Inference algorithms; Neural networks; Neurons; Numerical models; Partitioning algorithms; Power system modeling; Explanative characteristic; fuzzy rules; radial basis function (RBF) neural network;

fLanguage

English

Journal_Title

Industrial Electronics, IEEE Transactions on

Publisher

ieee

ISSN

0278-0046

Type

jour

DOI

10.1109/TIE.2006.878305

Filename

1667924